The present exemplary embodiments relate to Envisioning as it is understood in the Qualitative Reasoning area, and which is capable of investigating/modeling physical environments, systems and devices to produce causal explanations for behaviors of the physical environments, systems and devices. One use of the obtained explanations is to support decision making of future plans and for the execution of those plans. A useful discussion of Envisioning is set forth in the article by de Kleer, J. and Brown, J. S., 1982, “Foundations of Envisioning”, Proceedings Of The American Association For Artificial Intelligence, 1982 Aug. 16-20, which includes a discussion of an implemented envisioning system called ENVISION. Additional envisioning discussions are set out in de Kleer, J., and Brown, J. S. 1984, “A Qualitative Physics Based On Confluences”, Artificial Intelligence, 24(1):7-84; Forbus, K. D. 1984, “Qualitative Process Theory”, Artificial Intelligence 24(1):85-168, and Kuipers, B. J. 1986, “Qualitative Simulation”, Artificial Intelligence, 29(3):289-338. The foregoing articles are each incorporated by reference herein in their entirety.
Envisioning has been used extensively to model behavior of physical environments, systems, and devices. A simple example is a pressure regulator described in the de Kleer and Brown article. The modeling accomplished by Envisioning generates qualitatively distinct possible behaviors without numerically simulating every possible set of input conditions and model parameters.
Recently there has been an upsurge in research on adversarial reasoning, King G., Heeringa, B., Catalano, J., and Cohen, P., 2002, “Models Of Defeat”, 85-90, Proceedings Of The 2nd International Conference On Knowledge Systems For Coalition Operations; and Kott, A. and McEneasney, W., 2007, Adversarial Reasoning: Computational Approaches To Reading The Opponent's Mind, Chapman and Hall/CRC, but we are aware of no prior approaches which use qualitative representations extensively or perform envisioning. Cohen's Abstract Force Simulator, King, G., Heeringa, B., Catalano, J. and Cohen, P., 2002, “Models Of Defeat”, 85-90, Proceedings Of The 2nd International Conference On Knowledge Systems For Coalition Operations, uses numerical Monte Carlo simulation to identify qualitative regions in parameter spaces. Each of the foregoing articles is incorporated herein in their entirety.
Within the qualitative reasoning community, Clancy D., and Kuipers, B., 1997, “Model Decomposition And Simulation: A Component Based Qualitative Simulation Algorithm”, in Proc. AAAI, 97, 118-124, describes a qualitative simulator, Dec-SIM which partitions a system into non-interacting collections a priori using causal ordering (this article is hereby incorporated herein in its entirety). Later versions of Dec-SIM identify non-interacting collections dynamically but focus only on eliminating “chatter” when all interactions are known a priori.
The present application applies Envisioning to the analysis of course of action (COA) diagrams to determine qualitatively distinct outcomes of actions/operations in physical environments, systems and devices.